In Demand Response (DR) programs, utility companies experience strong need for accurate and reasonably fast predictions of customer energy loads in order to economically choose Demand Response signals scheduling and targeting, where a residential sector is an important target. Unlike commercial buildings, the residential sector contains a large number of customers such as individuals and families that typically are relatively small energy consumers. To deal with each small energy consuming customer individually is difficult and inefficient. The process for commercial and industrial (C&I) sector forecasting is not suitable for the residential sector, because the number of C&I customers is substantially lower and the energy demand of individual C&I customers is higher in orders of magnitude.
Modeling the energy demand of individual residential customers would lead to scalability issues, being computationally expensive. The accuracy of the predictions could be also quite low because of high variance of residential customer energy usage.